Course Content

Data Science Interview Challenge

## Data Science Interview Challenge

# Challenge 1: Visualizing Distributions

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to **visualize** the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

**Examine**the distribution of a dataset.**Visualize**the relationship between multiple variables.**Display**the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a **comprehensive view** of the data's distribution and its characteristics.

Task

Using Seaborn, visualize the distribution of a dataset:

- Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
- Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Task

Using Seaborn, visualize the distribution of a dataset:

- Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
- Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Everything was clear?

# Challenge 1: Visualizing Distributions

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to **visualize** the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

**Examine**the distribution of a dataset.**Visualize**the relationship between multiple variables.**Display**the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a **comprehensive view** of the data's distribution and its characteristics.

Task

Using Seaborn, visualize the distribution of a dataset:

- Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
- Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Task

Using Seaborn, visualize the distribution of a dataset:

Everything was clear?

# Challenge 1: Visualizing Distributions

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to **visualize** the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

**Examine**the distribution of a dataset.**Visualize**the relationship between multiple variables.**Display**the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a **comprehensive view** of the data's distribution and its characteristics.

Task

Using Seaborn, visualize the distribution of a dataset:

Task

Using Seaborn, visualize the distribution of a dataset:

Everything was clear?

**visualize** the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

**Examine**the distribution of a dataset.**Visualize**the relationship between multiple variables.**Display**the underlying probability distributions of datasets.

**comprehensive view** of the data's distribution and its characteristics.

Task

Using Seaborn, visualize the distribution of a dataset: